Our experiments test a simple solution to class imbalance that has been popular in the meta-learning community – Random-Shot meta-training [1]}, [2]}, [3]}. Contrarily to popular belief, our findings reveal that this method is scarcely effective when applied by itself.
Extensive analysis and validation performance through epochs (see Appendix ) suggest that these results are genuine and unlikely to be the result of inappropriate parameter tuning.
This finding has an important consequence for the meta-learning community, suggesting that robustness to imbalance cannot be obtained by the simple exposure to imbalanced tasks.
